- Since the beginning, data warehousingand Business Intelligence have been dominatedby insights into what happened in the past.For the first 10 years or so of the data warehousing eraalmost all BI was strategic in nature.We would report and analyze past resultsfrom the sales organization,how our products were doing out in the marketplace,the productivity of our workforce,or any other area of our business.In the early 2000s though, strategists not onlywanted visibility into the past, but also the present.

What became known as Operational Business Intelligencebecame popular in many organizationsin which the objective was to report and analyzecurrent results in time to take actionand actually affect the outcomerather than look after the fact,so hopefully we could do a better job next time.With Operational BI, we would look at howour production-line was actually doing in the middleof a shift versus its quota.We would examine customer activity,our systems and our networks looking for intrusionor other areas of our business,again, more operationally than strategically,so we could hopefully impact the outcomein a positive manner.

Operational Business Intelligence needs dataas quickly as possible,but the problem is that data warehouses aren't nearlytimely enough to meet those needs.This issue of data latency was addressed in a numberof different ways.In some cases, the batch ETL feeds were sped upas much as they possibly could beso daily feeds became hourly feeds,hourly feeds were done several times an hour,and in some cases this was enough to help bringdata in fast enough to perform Operational BI.

In other cases though, this wasn't sufficientand entirely new systems were built around real-timemessaging rather than our traditional batch TL feeds.In some cases, spinoff solutions, real-time data marts,were constructed that had no relationship whatsoeverwith enterprise data warehouses or any other data martsin the environment.And those did do a good job at addressing specific needsbut they weren't necessarily part of an overallarchitected solution across the enterprise.

We did a good job at increasing ingestion speed of data,but it came at a price with architectural mismatchacross the enterprise.As Big Data has become more popular and more powerful,it actually has done a great job at addressing these issuesof real-time Operational Business Intelligencewhere one of our three Vs for velocityhas played an important role in providingOperational BI to organizations.

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Released

8/6/2015

Big data and analytics have brought an entirely new era of data-driven insights to companies in all industries. Fortunately, those skilled in traditional business intelligence (BI) and data warehousing (DW) represent a fantastic pool of resources to help businesses adopt this new generation of technologies. This course will quickly educate BI/DW professionals in the key aspects of big data and analytics, including its evolution over the last two decades. Alan Simon shows how to take advantage of new architectures and technologies, such as Hadoop, and build on what you already know to plan a roadmap to a better big data solution for your business.